# Class5: Data visualization
# Today we are going to use ggplot2 packages to help us visualize data
#First we need to load the packages!
library(ggplot2)
# We will use this inbiult "cars" dataset first
head(cars)
## speed dist
## 1 4 2
## 2 4 10
## 3 7 4
## 4 7 22
## 5 8 16
## 6 9 10
# All ggplots have at least 3 layers,
# data + aes + geoms
ggplot(cars) +
aes(x=speed, y=dist) +
#geom_points develops scatter plot
#do different plots with geom_(type of graph you want)
#lm= linear model (linearizes data sets)
#smooth adds line of best regression
#method is used to argue what methods we want to see to visualize data
geom_point() +
geom_smooth(method="lm") +
#labs= labels that we will be adding to the data set
labs(title="Stopping Distance of Old Cars",
x="Speed (MPH)", y="Stopping Distance (ft)")
## `geom_smooth()` using formula 'y ~ x'
#Side-not: ggplot is not the only graphics system
#a very popular one is good old "base" R graphcs
url <- "https://bioboot.github.io/bimm143_S20/class-material/up_down_expression.txt"
genes <- read.delim(url)
head(genes)
## Gene Condition1 Condition2 State
## 1 A4GNT -3.6808610 -3.4401355 unchanging
## 2 AAAS 4.5479580 4.3864126 unchanging
## 3 AASDH 3.7190695 3.4787276 unchanging
## 4 AATF 5.0784720 5.0151916 unchanging
## 5 AATK 0.4711421 0.5598642 unchanging
## 6 AB015752.4 -3.6808610 -3.5921390 unchanging
nrow(genes)
## [1] 5196
colnames(genes)
## [1] "Gene" "Condition1" "Condition2" "State"
ncol(genes)
## [1] 4
table(genes$State)
##
## down unchanging up
## 72 4997 127
round(table(genes$State)/nrow(genes) * 100, 2)
##
## down unchanging up
## 1.39 96.17 2.44
ggplot(genes) +
aes(x=Condition1, y=Condition2, col=State) +
geom_point()
p <- ggplot(genes) +
aes(x=Condition1, y=Condition2, col=State) +
geom_point()
p
p+scale_color_manual(values = c("green", "antiquewhite4", "dark green")) +
labs(x="Control (No Drug Treatment)", y="Drug Treatment", title="Gene Expresion Changes Upon Drug Treatment")
#install.packages("gapminder")
library(gapminder)
ggplot(gapminder) + aes(x=year, y=lifeExp, col=continent) + geom_jitter(width=0.3, alpha=0.4) + geom_violin(aes(group=year), alpha=0.2, draw_quantiles=0.5)
#install.packages("plotly")
library (plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
ggplotly()
# install.packages("dplyr") ## uncoment to install if needed
#install.packages("dplyr")
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
gapminder_2007 <- gapminder %>% filter(year==2007)
gapminder_2007
## # A tibble: 142 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 2007 43.8 31889923 975.
## 2 Albania Europe 2007 76.4 3600523 5937.
## 3 Algeria Africa 2007 72.3 33333216 6223.
## 4 Angola Africa 2007 42.7 12420476 4797.
## 5 Argentina Americas 2007 75.3 40301927 12779.
## 6 Australia Oceania 2007 81.2 20434176 34435.
## 7 Austria Europe 2007 79.8 8199783 36126.
## 8 Bahrain Asia 2007 75.6 708573 29796.
## 9 Bangladesh Asia 2007 64.1 150448339 1391.
## 10 Belgium Europe 2007 79.4 10392226 33693.
## # … with 132 more rows
ggplot(gapminder_2007) +
aes(x=gdpPercap, y=lifeExp, size=pop) +
#aplha makes the points transparent
geom_point(alpha=0.5) +
scale_size_area(max_size = 10)
gapminder_1957 <- gapminder %>% filter(year==1957)
ggplot(gapminder_1957) +
aes(x = gdpPercap, y = lifeExp, color=continent,size = pop) +
geom_point(alpha=0.7) +
scale_size_area(max_size = 10)
gapminder_1957 <- gapminder %>% filter(year==1957 | year==2007)
# the |year ==2007 will add the data from 2007 next to 1957 data sets
ggplot(gapminder_1957) +
aes(x = gdpPercap, y = lifeExp, color=continent,size = pop) +
geom_point(alpha=0.7) +
scale_size_area(max_size = 10) +
facet_wrap(~year)
#You should now include the layer facet_wrap(~year) to produce the following plot:
gapminder_top5 <- gapminder %>%
filter(year==2007) %>%
arrange(desc(pop)) %>%
top_n(5, pop)
gapminder_top5
## # A tibble: 5 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 China Asia 2007 73.0 1318683096 4959.
## 2 India Asia 2007 64.7 1110396331 2452.
## 3 United States Americas 2007 78.2 301139947 42952.
## 4 Indonesia Asia 2007 70.6 223547000 3541.
## 5 Brazil Americas 2007 72.4 190010647 9066.
ggplot(gapminder_top5) +
geom_col(aes(x = country, y = pop, fill=continent))
ggplot(gapminder_top5) +
geom_col(aes(x = country, y = pop, fill=lifeExp))
# Plot population size by country
ggplot(gapminder_top5) +
aes(x=reorder (country, -pop), y=pop, fill=country) +
geom_col(col="gray30") +
guides(fill=FALSE)
## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.
#FLIPPING CHARTS
head(USArrests)
## Murder Assault UrbanPop Rape
## Alabama 13.2 236 58 21.2
## Alaska 10.0 263 48 44.5
## Arizona 8.1 294 80 31.0
## Arkansas 8.8 190 50 19.5
## California 9.0 276 91 40.6
## Colorado 7.9 204 78 38.7
USArrests$State <- rownames(USArrests)
ggplot(USArrests) +
aes(x=reorder(State,Murder), y=Murder) +
geom_col() +
coord_flip()
ggplot(USArrests) +
aes(x=reorder(State,Murder), y=Murder) +
geom_point() +
geom_segment(aes(x=State,
xend=State,
y=0,
yend=Murder), color="blue") +
coord_flip()
#install.packages("gifski")
#install.packages("gganimate")
library(gapminder)
library(gganimate)
# Setup nice regular ggplot of the gapminder data
ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, colour = country)) +
geom_point(alpha = 0.7, show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
scale_x_log10() +
# Facet by continent
facet_wrap(~continent) +
# Here comes the gganimate specific bits
labs(title = 'Year: {frame_time}', x = 'GDP per capita', y = 'life expectancy') +
transition_time(year) +
shadow_wake(wake_length = 0.1, alpha = FALSE)
#install.packages("patchwork")
library(patchwork)
# Setup some example plots
p1 <- ggplot(mtcars) + geom_point(aes(mpg, disp))
p2 <- ggplot(mtcars) + geom_boxplot(aes(gear, disp, group = gear))
p3 <- ggplot(mtcars) + geom_smooth(aes(disp, qsec))
p4 <- ggplot(mtcars) + geom_bar(aes(carb))
# Use patchwork to combine them here:
(p1 | p2 | p3) / p4
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
